SAA-C03 Design High-Performing Architectures Practice Question
A company runs a stateless web API on Amazon EC2 behind an Application Load Balancer. The team notices that during business hours, the ALB starts queueing requests and the average request latency rises. They want to scale out quickly and reliably based on demand, not CPU alone. Which Auto Scaling approach best matches this requirement?
⚠ Common exam trap
Test-takers frequently default to CPU-based scaling (a common but incomplete metric) or memory-based scaling, overlooking that for a stateless web API behind an ALB, request count per target is the most direct indicator of demand and latency issues.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use target tracking scaling based on ALB request count per target.
Target tracking scaling based on ALB request count per target directly measures the load on each instance, allowing the Auto Scaling group to add or remove instances to maintain a target value. This approach scales out quickly and reliably based on actual demand (request queuing and latency), not just CPU, which aligns with the requirement to respond to rising latency and queueing during business hours.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a fixed-size Auto Scaling group and increase capacity manually once per hour.
Why it's wrong here
This is manual and slow, which can’t react to sudden traffic spikes effectively.
- ✓
Use target tracking scaling based on ALB request count per target.
Why this is correct
Target tracking can automatically adjust capacity using ALB load metrics and respond faster.
- ✗
Scale based only on EC2 instance memory utilization, regardless of load.
Why it's wrong here
Memory utilization may not correlate with queueing or request latency, causing mismatched scaling.
- ✗
Use step scaling with a single threshold on average network-in bytes.
Why it's wrong here
Step scaling can work, but networking thresholds often lag behind request queuing and latency issues.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This SAA-C03 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the SAA-C03 exam.